Anomia is a core feature of aphasia, a disorder that affects approximately 80,000 people each year. Sensitive metrics of change are needed for the development and evaluation of new anomia treatments and to provide the basis for rehabilitation reimbursement and policy decisions. To be optimally useful, these metrics must (i) be validated within a modern framework, such as item response theory, to enhance their psychometric properties, (ii) yield information about severity and underlying cognitive deficits with low response burden, (iii) support repeated assessments without posing threats to internal validity, and (iv) predict functioning at the activity level of the ICF. The long-term goal of thisline of research is to maximize the precision, efficiency, and utility of word retrieval assessment for quantifying change, evaluating new therapeutic protocols, and diagnosing impairments in terms of current theoretical models. To achieve this goal, this proposal will focus on engineering a theoretically driven, computer-adaptive system using IRT for assessing anomia. IRT is a state-of-the-art psychometric framework with important theoretical and practical benefits. First, it transforms observed responses to test items into latent trait scores with interval properties. Second, it formalizes how measurement precision varies as a function of a person's ability. Third, it supports algorithms for computer adaptive testing (CAT), in which only the most informative items are selected and administered for a particular patient, thus reducing testing time. It also permits the generation of equivalent test forms for assessing the same person on multiple occasions with scores that are expressed on a single common scale. This proposal has the following Specific Aims. Specific Aim 1a is to validate a CAT version of the Philadelphia Naming Test (PNT-CAT) that is based on a 1-parameter logistic IRT model for quantifying overall severity. Specifically, this aim will determine the extent to which CAT-PNT scores are precise, unbiased, and correlated with performance on the full PNT. Specific Aim 1b is to determine the ability of an augmented CAT algorithm to predict error proportions on the full PNT. The goal of Specific Aim 2 is to verify the equivalence and precision of PNT-CAT alternate forms in order to determine stability when no change is expected. Specific Aim 3 will determine the relationship of the full PNT with discourse performance and assess any potential degradation of the strength of the relationship when CAT-PNT - instead of the full PNT - are used. Our overarching goal is consistent with the NIH Roadmap's recognition of the pressing need to better quantify clinically important symptoms and outcomes. Further, it directly addresses goals identified in the NIDCD Strategic Plan for 2012-2016, including the development of effective and efficient diagnostic tools by using electronic health data to inform the conscientious, explicit, and judicious use of current best evidence in making decisions about health care options to improve outcomes for individuals with communication disorders.